






import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 				A_     (    __lowercase			, unittest.TestCase      ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE				:      str			=       LongformerTokenizer
 _SCREAMING_SNAKE_CASE				:      Union[str, Any]			=       True
 _SCREAMING_SNAKE_CASE				:      Tuple			=       LongformerTokenizerFast
 _SCREAMING_SNAKE_CASE				:      str			=       True


 def 			snake_case__					(						self)			->		int:

     """simple docstring"""



     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     _UpperCAmelCase					:   int      	=					[
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''\u0120''',
         '''\u0120l''',
         '''\u0120n''',
         '''\u0120lo''',
         '''\u0120low''',
         '''er''',
         '''\u0120lowest''',
         '''\u0120newer''',
         '''\u0120wider''',
         '''<unk>''',
     ]
     _UpperCAmelCase					:   List[Any]      	=					dict(zip(_A				,	range(len(_A))))
     _UpperCAmelCase					:   List[Any]      	=					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
     _UpperCAmelCase					:   Optional[Any]      	=					{'''unk_token''': '''<unk>'''}

     _UpperCAmelCase					:   List[Any]      	=					os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['''vocab_file'''])
     _UpperCAmelCase					:   Optional[Any]      	=					os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['''merges_file'''])
     with open(self.vocab_file				,	'''w'''				,	encoding='''utf-8''') as fp:
         fp.write(json.dumps(_A) + '''\n''')
     with open(self.merges_file				,	'''w'''				,	encoding='''utf-8''') as fp:
         fp.write('''\n'''.join(_A))


 def 			snake_case__					(						self				,	**_A)			->		str:

     """simple docstring"""



     kwargs.update(self.special_tokens_map)
     return self.tokenizer_class.from_pretrained(self.tmpdirname				,	**_A)


 def 			snake_case__					(						self				,	**_A)			->		List[str]:

     """simple docstring"""



     kwargs.update(self.special_tokens_map)
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,	**_A)


 def 			snake_case__					(						self				,	_A)			->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase					:   str      	=					'''lower newer'''
     _UpperCAmelCase					:   Tuple      	=					'''lower newer'''
     return input_text, output_text


 def 			snake_case__					(						self)			->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase					:   Optional[int]      	=					self.tokenizer_class(self.vocab_file				,	self.merges_file				,	**self.special_tokens_map)
     _UpperCAmelCase					:   str      	=					'''lower newer'''
     _UpperCAmelCase					:   str      	=					['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
     _UpperCAmelCase					:   Dict      	=					tokenizer.tokenize(_A)  # , add_prefix_space=True)
     self.assertListEqual(_A				,	_A)

     _UpperCAmelCase					:   Union[str, Any]      	=					tokens + [tokenizer.unk_token]
     _UpperCAmelCase					:   Optional[int]      	=					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)				,	_A)


 def 			snake_case__					(						self)			->		Any:

     """simple docstring"""



     _UpperCAmelCase					:   Tuple      	=					self.get_tokenizer()

     self.assertListEqual(tokenizer.encode('''Hello world!'''				,	add_special_tokens=_A)				,	[0, 31414, 232, 328, 2])
     self.assertListEqual(
         tokenizer.encode('''Hello world! cécé herlolip 418'''				,	add_special_tokens=_A)				,	[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]				,	)


 @slow
 def 			snake_case__					(						self)			->		Dict:

     """simple docstring"""



     _UpperCAmelCase					:   Dict      	=					self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096''')

     _UpperCAmelCase					:   List[Any]      	=					tokenizer.encode('''sequence builders'''				,	add_special_tokens=_A)
     _UpperCAmelCase					:   Any      	=					tokenizer.encode('''multi-sequence build'''				,	add_special_tokens=_A)

     _UpperCAmelCase					:   int      	=					tokenizer.encode(
         '''sequence builders'''				,	add_special_tokens=_A				,	add_prefix_space=_A)
     _UpperCAmelCase					:   Optional[int]      	=					tokenizer.encode(
         '''sequence builders'''				,	'''multi-sequence build'''				,	add_special_tokens=_A				,	add_prefix_space=_A)

     _UpperCAmelCase					:   Dict      	=					tokenizer.build_inputs_with_special_tokens(_A)
     _UpperCAmelCase					:   Dict      	=					tokenizer.build_inputs_with_special_tokens(_A				,	_A)

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode


 def 			snake_case__					(						self)			->		List[str]:

     """simple docstring"""



     _UpperCAmelCase					:   List[str]      	=					self.get_tokenizer()

     _UpperCAmelCase					:   Dict      	=					'''Encode this sequence.'''
     _UpperCAmelCase					:   int      	=					tokenizer.byte_encoder[''' '''.encode('''utf-8''')[0]]

     # Testing encoder arguments
     _UpperCAmelCase					:   Dict      	=					tokenizer.encode(_A				,	add_special_tokens=_A				,	add_prefix_space=_A)
     _UpperCAmelCase					:   Tuple      	=					tokenizer.convert_ids_to_tokens(encoded[0])[0]
     self.assertNotEqual(_A				,	_A)

     _UpperCAmelCase					:   int      	=					tokenizer.encode(_A				,	add_special_tokens=_A				,	add_prefix_space=_A)
     _UpperCAmelCase					:   List[Any]      	=					tokenizer.convert_ids_to_tokens(encoded[0])[0]
     self.assertEqual(_A				,	_A)

     tokenizer.add_special_tokens({'''bos_token''': '''<s>'''})
     _UpperCAmelCase					:   List[Any]      	=					tokenizer.encode(_A				,	add_special_tokens=_A)
     _UpperCAmelCase					:   Optional[int]      	=					tokenizer.convert_ids_to_tokens(encoded[1])[0]
     self.assertNotEqual(_A				,	_A)

     # Testing spaces after special tokens
     _UpperCAmelCase					:   Any      	=					'''<mask>'''
     tokenizer.add_special_tokens(
         {'''mask_token''': AddedToken(_A				,	lstrip=_A				,	rstrip=_A)})  # mask token has a left space
     _UpperCAmelCase					:   str      	=					tokenizer.convert_tokens_to_ids(_A)

     _UpperCAmelCase					:   List[Any]      	=					'''Encode <mask> sequence'''
     _UpperCAmelCase					:   int      	=					'''Encode <mask>sequence'''

     _UpperCAmelCase					:   List[str]      	=					tokenizer.encode(_A)
     _UpperCAmelCase					:   Optional[Any]      	=					encoded.index(_A)
     _UpperCAmelCase					:   int      	=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
     self.assertEqual(_A				,	_A)

     _UpperCAmelCase					:   int      	=					tokenizer.encode(_A)
     _UpperCAmelCase					:   Any      	=					encoded.index(_A)
     _UpperCAmelCase					:   Any      	=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
     self.assertNotEqual(_A				,	_A)


 def 			snake_case__					(						self)			->		Dict:

     """simple docstring"""



     pass


 def 			snake_case__					(						self)			->		Dict:

     """simple docstring"""



     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
             _UpperCAmelCase					:   Union[str, Any]      	=					self.rust_tokenizer_class.from_pretrained(_A				,	**_A)
             _UpperCAmelCase					:   List[Any]      	=					self.tokenizer_class.from_pretrained(_A				,	**_A)
             _UpperCAmelCase					:   List[Any]      	=					'''A, <mask> AllenNLP sentence.'''
             _UpperCAmelCase					:   List[Any]      	=					tokenizer_r.encode_plus(_A				,	add_special_tokens=_A				,	return_token_type_ids=_A)
             _UpperCAmelCase					:   Optional[Any]      	=					tokenizer_p.encode_plus(_A				,	add_special_tokens=_A				,	return_token_type_ids=_A)

             # token_type_ids should put 0 everywhere
             self.assertEqual(sum(tokens_r['''token_type_ids'''])				,	sum(tokens_p['''token_type_ids''']))

             # attention_mask should put 1 everywhere, so sum over length should be 1
             self.assertEqual(
                 sum(tokens_r['''attention_mask''']) / len(tokens_r['''attention_mask'''])				,	sum(tokens_p['''attention_mask''']) / len(tokens_p['''attention_mask'''])				,	)

             _UpperCAmelCase					:   Tuple      	=					tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''])
             _UpperCAmelCase					:   Any      	=					tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''])

             # Rust correctly handles the space before the mask while python doesnt
             self.assertSequenceEqual(tokens_p['''input_ids''']				,	[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
             self.assertSequenceEqual(tokens_r['''input_ids''']				,	[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

             self.assertSequenceEqual(
                 _A				,	['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''])
             self.assertSequenceEqual(
                 _A				,	['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''])


 def 			snake_case__					(						self)			->		int:

     """simple docstring"""



     for trim_offsets, add_prefix_space in itertools.product([True, False]				,	repeat=2):
         _UpperCAmelCase					:   int      	=					self.rust_tokenizer_class.from_pretrained(
             self.tmpdirname				,	use_fast=_A				,	add_prefix_space=_A				,	trim_offsets=_A)

         _UpperCAmelCase					:   Dict      	=					json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
         _UpperCAmelCase					:   Any      	=					json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

         self.assertEqual(pre_tokenizer_state['''add_prefix_space''']				,	_A)

         self.assertEqual(post_processor_state['''add_prefix_space''']				,	_A)
         self.assertEqual(post_processor_state['''trim_offsets''']				,	_A)


 def 			snake_case__					(						self)			->		Tuple:

     """simple docstring"""



     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
             _UpperCAmelCase					:   Dict      	=					'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
             _UpperCAmelCase					:   str      	=					f'''{text_of_1_token} {text_of_1_token}'''

             _UpperCAmelCase					:   Dict      	=					self.rust_tokenizer_class.from_pretrained(
                 _A				,	use_fast=_A				,	add_prefix_space=_A				,	trim_offsets=_A)
             _UpperCAmelCase					:   Optional[Any]      	=					tokenizer_r(_A				,	return_offsets_mapping=_A				,	add_special_tokens=_A)
             self.assertEqual(encoding.offset_mapping[0]				,	(0, len(_A)))
             self.assertEqual(
                 encoding.offset_mapping[1]				,	(len(_A) + 1, len(_A) + 1 + len(_A))				,	)

             _UpperCAmelCase					:   Dict      	=					self.rust_tokenizer_class.from_pretrained(
                 _A				,	use_fast=_A				,	add_prefix_space=_A				,	trim_offsets=_A)
             _UpperCAmelCase					:   str      	=					tokenizer_r(_A				,	return_offsets_mapping=_A				,	add_special_tokens=_A)
             self.assertEqual(encoding.offset_mapping[0]				,	(0, len(_A)))
             self.assertEqual(
                 encoding.offset_mapping[1]				,	(len(_A) + 1, len(_A) + 1 + len(_A))				,	)

             _UpperCAmelCase					:   Dict      	=					self.rust_tokenizer_class.from_pretrained(
                 _A				,	use_fast=_A				,	add_prefix_space=_A				,	trim_offsets=_A)
             _UpperCAmelCase					:   Union[str, Any]      	=					tokenizer_r(_A				,	return_offsets_mapping=_A				,	add_special_tokens=_A)
             self.assertEqual(encoding.offset_mapping[0]				,	(0, len(_A)))
             self.assertEqual(
                 encoding.offset_mapping[1]				,	(len(_A), len(_A) + 1 + len(_A))				,	)

             _UpperCAmelCase					:   Tuple      	=					self.rust_tokenizer_class.from_pretrained(
                 _A				,	use_fast=_A				,	add_prefix_space=_A				,	trim_offsets=_A)
             _UpperCAmelCase					:   str      	=					tokenizer_r(_A				,	return_offsets_mapping=_A				,	add_special_tokens=_A)
             self.assertEqual(encoding.offset_mapping[0]				,	(0, len(_A)))
             self.assertEqual(
                 encoding.offset_mapping[1]				,	(len(_A), len(_A) + 1 + len(_A))				,	)

             _UpperCAmelCase					:   int      	=					f''' {text}'''

             # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
             #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
             # )
             # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
             # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
             # self.assertEqual(
             #     encoding.offset_mapping[1],
             #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
             # )

             _UpperCAmelCase					:   List[str]      	=					self.rust_tokenizer_class.from_pretrained(
                 _A				,	use_fast=_A				,	add_prefix_space=_A				,	trim_offsets=_A)
             _UpperCAmelCase					:   Dict      	=					tokenizer_r(_A				,	return_offsets_mapping=_A				,	add_special_tokens=_A)
             self.assertEqual(encoding.offset_mapping[0]				,	(1, 1 + len(_A)))
             self.assertEqual(
                 encoding.offset_mapping[1]				,	(1 + len(_A) + 1, 1 + len(_A) + 1 + len(_A))				,	)

             _UpperCAmelCase					:   int      	=					self.rust_tokenizer_class.from_pretrained(
                 _A				,	use_fast=_A				,	add_prefix_space=_A				,	trim_offsets=_A)
             _UpperCAmelCase					:   Optional[Any]      	=					tokenizer_r(_A				,	return_offsets_mapping=_A				,	add_special_tokens=_A)
             self.assertEqual(encoding.offset_mapping[0]				,	(0, 1 + len(_A)))
             self.assertEqual(
                 encoding.offset_mapping[1]				,	(1 + len(_A), 1 + len(_A) + 1 + len(_A))				,	)

             _UpperCAmelCase					:   Optional[Any]      	=					self.rust_tokenizer_class.from_pretrained(
                 _A				,	use_fast=_A				,	add_prefix_space=_A				,	trim_offsets=_A)
             _UpperCAmelCase					:   Optional[int]      	=					tokenizer_r(_A				,	return_offsets_mapping=_A				,	add_special_tokens=_A)
             self.assertEqual(encoding.offset_mapping[0]				,	(0, 1 + len(_A)))
             self.assertEqual(
                 encoding.offset_mapping[1]				,	(1 + len(_A), 1 + len(_A) + 1 + len(_A))				,	)












'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					StableDiffusionInstructPixaPixPipeline
					a_        =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
					a_        =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					a_        =					IMAGE_TO_IMAGE_IMAGE_PARAMS
					a_        =					IMAGE_TO_IMAGE_IMAGE_PARAMS




					def 		A      (  self  :		str      )    ->							Optional[Any]:
								torch.manual_seed(0      )
								UpperCAmelCase_      :       Tuple          =       UNetaDConditionModel(
								    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=8				, out_channels=4				, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				, cross_attention_dim=32				, )
								UpperCAmelCase_      :       Any          =       PNDMScheduler(skip_prk_steps=_A      )
								torch.manual_seed(0      )
								UpperCAmelCase_      :       int          =       AutoencoderKL(
								    block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				, latent_channels=4				, )
								torch.manual_seed(0      )
								UpperCAmelCase_      :       Tuple          =       CLIPTextConfig(
								    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1e-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=10_00				, )
								UpperCAmelCase_      :       List[str]          =       CLIPTextModel(_A      )
								UpperCAmelCase_      :       List[str]          =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

								UpperCAmelCase_      :       Union[str, Any]          =       {
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components




					def 		A      (  self  :		List[str]				, _A  :		Tuple				, _A  :		str=0      )    ->							int:
								UpperCAmelCase_      :       Tuple          =       floats_tensor((1, 3, 32, 32)				, rng=random.Random(_A      )      ).to(_A      )
								UpperCAmelCase_      :       Dict          =       image.cpu().permute(0				, 2				, 3				, 1      )[0]
								UpperCAmelCase_      :       Optional[Any]          =       Image.fromarray(np.uinta(_A      )      ).convert('''RGB'''      )
								if str(_A      ).startswith('''mps'''      ):
											UpperCAmelCase_      :       str          =       torch.manual_seed(_A      )
								else:
											UpperCAmelCase_      :       Optional[Any]          =       torch.Generator(device=_A      ).manual_seed(_A      )
								UpperCAmelCase_      :       List[str]          =       {
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''image_guidance_scale''': 1,
								    '''output_type''': '''numpy''',
								}
								return inputs




					def 		A      (  self  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       Any          =       self.get_dummy_components()
								UpperCAmelCase_      :       int          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       Tuple          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       str          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       sd_pipe(**_A      ).images
								UpperCAmelCase_      :       int          =       image[0, -3:, -3:, -1]
								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase_      :       Union[str, Any]          =       np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		int      )    ->							str:
								UpperCAmelCase_      :       List[Any]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       List[str]          =       self.get_dummy_components()
								UpperCAmelCase_      :       Dict          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       str          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       Any          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       List[Any]          =       '''french fries'''
								UpperCAmelCase_      :       Optional[Any]          =       sd_pipe(**_A				, negative_prompt=_A      )
								UpperCAmelCase_      :       Tuple          =       output.images
								UpperCAmelCase_      :       Union[str, Any]          =       image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase_      :       Dict          =       np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		Dict      )    ->							str:
								UpperCAmelCase_      :       List[str]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       List[Any]          =       self.get_dummy_components()
								UpperCAmelCase_      :       Any          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       int          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       Any          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       Any          =       [inputs['''prompt''']] * 2

								UpperCAmelCase_      :       int          =       np.array(inputs['''image''']      ).astype(np.floataa      ) / 255.0
								UpperCAmelCase_      :       int          =       torch.from_numpy(_A      ).unsqueeze(0      ).to(_A      )
								UpperCAmelCase_      :       List[str]          =       image / 2 + 0.5
								UpperCAmelCase_      :       str          =       image.permute(0				, 3				, 1				, 2      )
								UpperCAmelCase_      :       Any          =       image.repeat(2				, 1				, 1				, 1      )

								UpperCAmelCase_      :       List[Any]          =       sd_pipe(**_A      ).images
								UpperCAmelCase_      :       str          =       image[-1, -3:, -3:, -1]

								assert image.shape == (2, 32, 32, 3)
								UpperCAmelCase_      :       Optional[Any]          =       np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		List[str]      )    ->							List[Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       Tuple          =       self.get_dummy_components()
								UpperCAmelCase_      :       List[str]          =       EulerAncestralDiscreteScheduler(
								    beta_start=0.00_085				, beta_end=0.012				, beta_schedule='''scaled_linear'''      )
								UpperCAmelCase_      :       List[Any]          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       Dict          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       Tuple          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       str          =       sd_pipe(**_A      ).images
								UpperCAmelCase_      :       Dict          =       image[0, -3:, -3:, -1]

								UpperCAmelCase_      :       Dict          =       [round(_A				, 4      ) for x in image_slice.flatten().tolist()]
								print(''','''.join([str(_A      ) for x in slice]      )      )

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase_      :       Any          =       np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		str      )    ->							List[str]:
								super().test_inference_batch_single_identical(expected_max_diff=3e-3      )





					def 		A      (  self  :		List[Any]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Tuple          =       self.get_dummy_components()
								UpperCAmelCase_      :       Tuple          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       Any          =       VaeImageProcessor(do_resize=_A				, do_normalize=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       int          =       pipe(**self.get_dummy_inputs_by_type(_A				, input_image_type='''pt'''      )      )[0]

								UpperCAmelCase_      :       Union[str, Any]          =       components['''vae''']
								UpperCAmelCase_      :       Any          =       self.get_dummy_inputs_by_type(_A				, input_image_type='''pt'''      )

								for image_param in self.image_latents_params:
											if image_param in inputs.keys():
														UpperCAmelCase_      :       int          =       vae.encode(inputs[image_param]      ).latent_dist.mode()

								UpperCAmelCase_      :       List[Any]          =       pipe(**_A      )[0]

								UpperCAmelCase_      :       int          =       np.abs(out - out_latents_inputs      ).max()
								self.assertLess(_A				, 1e-4				, '''passing latents as image input generate different result from passing image'''      )






@slow
@require_torch_gpu
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Any      )    ->							Optional[Any]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




					def 		A      (  self  :		List[str]				, _A  :		Optional[Any]=0      )    ->							List[Any]:
								UpperCAmelCase_      :       Dict          =       torch.manual_seed(_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''      )
								UpperCAmelCase_      :       Optional[int]          =       {
								    '''prompt''': '''turn him into a cyborg''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 3,
								    '''guidance_scale''': 7.5,
								    '''image_guidance_scale''': 1.0,
								    '''output_type''': '''numpy''',
								}
								return inputs




					def 		A      (  self  :		Optional[int]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       List[Any]          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A      )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       List[str]          =       self.get_inputs()
								UpperCAmelCase_      :       int          =       pipe(**_A      ).images
								UpperCAmelCase_      :       Dict          =       image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCAmelCase_      :       Any          =       np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]      )

								assert np.abs(expected_slice - image_slice      ).max() < 1e-3




					def 		A      (  self  :		Optional[Any]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[Any]          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A      )
								UpperCAmelCase_      :       Dict          =       LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       List[Any]          =       self.get_inputs()
								UpperCAmelCase_      :       str          =       pipe(**_A      ).images
								UpperCAmelCase_      :       Optional[int]          =       image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCAmelCase_      :       List[str]          =       np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]      )

								assert np.abs(expected_slice - image_slice      ).max() < 1e-3




					def 		A      (  self  :		List[str]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       str          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A      )
								UpperCAmelCase_      :       str          =       DDIMScheduler.from_config(pipe.scheduler.config      )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       Any          =       self.get_inputs()
								UpperCAmelCase_      :       Any          =       pipe(**_A      ).images
								UpperCAmelCase_      :       Optional[Any]          =       image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCAmelCase_      :       Dict          =       np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]      )

								assert np.abs(expected_slice - image_slice      ).max() < 1e-3




					def 		A      (  self  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       0

								def callback_fn(_A  :		int				, _A  :		int				, _A  :		torch.FloatTensor      ) -> None:
											UpperCAmelCase_      :       Tuple          =       True
											nonlocal number_of_steps
											number_of_steps += 1
											if step == 1:
														UpperCAmelCase_      :       List[str]          =       latents.detach().cpu().numpy()
														assert latents.shape == (1, 4, 64, 64)
														UpperCAmelCase_      :       Optional[Any]          =       latents[0, -3:, -3:, -1]
														UpperCAmelCase_      :       List[Any]          =       np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]      )

														assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2
											elif step == 2:
														UpperCAmelCase_      :       Union[str, Any]          =       latents.detach().cpu().numpy()
														assert latents.shape == (1, 4, 64, 64)
														UpperCAmelCase_      :       Dict          =       latents[0, -3:, -3:, -1]
														UpperCAmelCase_      :       Tuple          =       np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]      )

														assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2

								UpperCAmelCase_      :       Dict          =       False

								UpperCAmelCase_      :       Dict          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A				, torch_dtype=torch.floataa      )
								UpperCAmelCase_      :       Any          =       pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       Dict          =       self.get_inputs()
								pipe(**_A				, callback=_A				, callback_steps=1      )
								assert callback_fn.has_been_called
								assert number_of_steps == 3




					def 		A      (  self  :		int      )    ->							int:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								UpperCAmelCase_      :       Union[str, Any]          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A				, torch_dtype=torch.floataa      )
								UpperCAmelCase_      :       Tuple          =       pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing(1      )
								pipe.enable_sequential_cpu_offload()

								UpperCAmelCase_      :       Dict          =       self.get_inputs()
								UpperCAmelCase_      :       List[str]          =       pipe(**_A      )

								UpperCAmelCase_      :       Dict          =       torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 10**9





					def 		A      (  self  :		str      )    ->							int:
								UpperCAmelCase_      :       List[Any]          =       self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								UpperCAmelCase_      :       List[str]          =       inputs['''image'''].resize((5_04, 5_04)      )

								UpperCAmelCase_      :       int          =       '''timbrooks/instruct-pix2pix'''
								UpperCAmelCase_      :       Tuple          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    _A				, safety_checker=_A				, )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       Optional[Any]          =       pipe(**_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       output.images[0]

								UpperCAmelCase_      :       List[str]          =       image[2_55:2_58, 3_83:3_86, -1]

								assert image.shape == (5_04, 5_04, 3)
								UpperCAmelCase_      :       List[str]          =       np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-3


